Data from: Retracing the response of Rangifer to postglacial climate change in Arctic islands
Bibliographic record
Abstract
We compiled published datasets of mitochondrial DNA sequences which informed Quaternary population history scenarios. These scenarios were evaluated in a coalescent-based approximate Bayesian computation (ABC) modelling framework (using DIYABC RF) to test hypotheses of postglacial island (re)colonisation and to estimate timings of population divergence and admixture. Population events were compared with modelled paleo-sea ice cover and published ice sheet chronologies. Rangifer tarandus L. play a key role in Arctic ecosystems as the most numerous and widespread large herbivore. Sea ice is vital for maintaining genetic connectivity in Arctic islands, yet the historical role of sea ice in shaping R. tarandus biogeography is unknown. We studied the role of sea ice changes and ice sheet retreat since the last glacial period in the timing of island dispersal. We compiled published datasets of mitochondrial control region sequences that informed population history scenarios, which were evaluated in a coalescent-based approximate Bayesian computation (ABC) modelling framework to test hypotheses of island (re)colonisation and to estimate divergence and admixture. Population events were compared with modelled and proxy-based paleo-sea ice cover and published ice sheet chronologies. Our analysis supports Holocene dispersal onto deglaciated Arctic islands, rather than High Arctic glacial refugia. The degree of population admixture and the effect of sea ice was dependent on regional geography and climate history. North American initial island population divergence occurred as sea ice cover was declining. A lack of strong genetic structure and the occurrence of late Holocene admixture suggest that Canadian Arctic Archipelago populations were somewhat connected by sea ice during the Holocene. The Svalbard, Franz Josef land, and West Greenland colonisations arose through long-distance dispersal. Here, divergence times occurred post-deglaciation but broadly align with subfossil-based colonisation estimates, suggesting dispersal limitation due to sea ice conditions, potentially requiring appropriate ocean currents and sea ice drift directionality and speeds. Our study sheds light on the Late Quaternary (~60 ka - present) history of Arctic island Rangifer and suggests that ice sheet retreat, sea ice, and ocean currents were important in shaping present-day genetic patterns. Regional differences in postglacial dynamics suggest that dispersal during contemporary climate change may vary regionally and depend upon diminishing connectivity provided by sea ice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".